Academic literature on the topic 'Multisensor'

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Journal articles on the topic "Multisensor"

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Nur, Rachman Supadmana Muda, Faranadila Bilqis, and Fadilah M.Faisal. "Implementation of Multisensor to Detect Vibration, Sound and Image of Combat Vehicles Use Artificial Neural Networks." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 2 (2024): 7. https://doi.org/10.5281/zenodo.10720432.

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Multisensors in general used in the military world as system sensing far to aim For detect existence object Good sound , vibration nor image around with use wave electromagnetic . With do application computing systematic, will obtained A visual depiction of representation from medium object observed. Implementation Multisensor can utilise principle radiation and reflection wave electromagnetic or wave sound on energy levels and intervals time certain. With do application computing systematic, will obtained a visual depiction of representation from medium object observed. Although modern multis
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Lin, Tsun-Kuo. "PCA/SVM-Based Method for Pattern Detection in a Multisensor System." Mathematical Problems in Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/6486345.

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This paper presents a multivariate analysis framework for pattern detection in a multisensor system; the proposed principal component analysis (PCA)/support vector machine- (SVM-) based supervision scheme can identify patterns in the multisensory system. Although the PCA and SVM are commonly used in pattern recognition, an effective methodology using the PCA/SVM for multisensory system remains unexplored. Pattern detection in a multisensor system has long been a challenge. For example, object inspections in multisensor systems are difficult to perform because inspectors might fail to use multi
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Zenevich, A. O., S. V. Zhdanovich, H. V. Vasilevski, A. A. Lagutik, T. G. Kovalenko, and T. M. Lukashik. "Research of multisensor characteristics based on optical fiber." Doklady BGUIR 19, no. 1 (2021): 70–78. http://dx.doi.org/10.35596/1729-7648-2021-19-1-70-78.

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The research results of multisensors based on optical fiber, the principle of which is to change the conditions of propagation of optical radiation in the optical fiber in the places where macro-bends are formed at the points of impact, are presented in the paper. The formation of macro-bends leads to an additional attenuation of the power of optical radiation propagating through the optical fiber. A single-mode optical fiber was used with the parameters, which are supported by numerous manufacturers and comply with the recommendations of ITU-T G.655. The measurements were carried out for four
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Bryndin, Evgeniy. "Creation of multimodal digital twins with reflexive AGI multilogic and multisensory." Research on Intelligent Manufacturing and Assembly 2, no. 1 (2024): 85–93. http://dx.doi.org/10.25082/rima.2023.01.005.

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Reflexive AGI multilogic, multimodality and multisensors are the basis for the multidisciplinary development of intelligent digital twins. Multimodality is implemented in several formats: text, image, speech, formulas, etc. Multilogic is implemented according to several rules or methods or ways of working with knowledge and data, and the criterion for selecting the best result from all implementations. AGI multilogic carries out judgment, understanding, perception, comparison, analysis, choice, etc. Understanding is realized using the technology of unified objectification of the ontology of su
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Bryndin, Evgeny. "Theoretical Aspects of Creating Multimodal Digital Twins with AGI Multilogic and Multisensory." Journal of Progress in Engineering and Physical Science 3, no. 3 (2024): 40–47. http://dx.doi.org/10.56397/jpeps.2024.09.05.

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AGI multilogic, multimodality and multisensors are the basis for the multidisciplinary development of intelligent digital twins. Multimodality is implemented in several formats: text, image, speech, formulas, etc. Multilogic is implemented according to several rules or methods or ways of working with knowledge and data, and the criterion for selecting the best result from all implementations. AGI multilogic carries out judgment, understanding, perception, comparison, analysis, choice, etc. Understanding is realized using the technology of unified objectification of the ontology of subject area
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Bogomolov, Andrey. "Developing Multisensory Approach to the Optical Spectral Analysis." Sensors 21, no. 10 (2021): 3541. http://dx.doi.org/10.3390/s21103541.

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This article presents an overview of research aimed at developing a scientific approach to creating multisensor optical systems for chemical analysis. The review is mainly based on the author’s works accomplished over the recent 10 years at Samara State Technical University with broad international cooperation. It consists of an introduction and five sections that describe state of the art in the field of optical sensing, suggested development methodology of optical multisensor systems, related aspects of experimental design and process analytical technology followed by a collection of practic
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Sanjaya, Muhammad Fahyu, Ummu Kalsum, and Andi Rosman N. "PENERAPAN TEKNOLOGI CERDAS PENYIRAMAN TANAMAN HIDROPONIK BERBASIS MIKROKONTROLER DAN MULTISENSOR PADA PEMBUDIDAYA TANAMAN HIDROPONIK KABUPATEN MAJENE." Jurnal Abdi Insani 10, no. 3 (2023): 1880–89. http://dx.doi.org/10.29303/abdiinsani.v10i3.1113.

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Smart technology has provided many benefits for all human activities, including hydroponic farming systems. The consistent challenge in the process of cultivating plants using hydroponic systems is the monitoring of partner farmers in managing plant nutrition. Smart technology, utilizing microcontrollers and multisensors, can enhance hydroponic management, particularly concerning the control of nutrient availability, including saturation, water pH, and water availability in the hydroponic system. The purpose of this activity is to address partner-related issues regarding their lack of knowledg
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LIU, QING (CHARLIE), and HSU-PIN (BEN) WANG. "A case study on multisensor data fusion for imbalance diagnosis of rotating machinery." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 3 (2001): 203–10. http://dx.doi.org/10.1017/s0890060401153011.

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Techniques for machine condition monitoring and diagnostics are gaining acceptance in various industrial sectors. They have proved to be effective in predictive or proactive maintenance and quality control. Along with the fast development of computer and sensing technologies, sensors are being increasingly used to monitor machine status. In recent years, the fusion of multisensor data has been applied to diagnose machine faults. In this study, multisensors are used to collect signals of rotating imbalance vibration of a test rig. The characteristic features of each vibration signal are extract
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Han, Youkyung, Jaewan Choi, Jinha Jung, Anjin Chang, Sungchan Oh, and Junho Yeom. "Automated Coregistration of Multisensor Orthophotos Generated from Unmanned Aerial Vehicle Platforms." Journal of Sensors 2019 (April 14, 2019): 1–10. http://dx.doi.org/10.1155/2019/2962734.

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Image coregistration is a key preprocessing step to ensure the effective application of very-high-resolution (VHR) orthophotos generated from multisensor images acquired from unmanned aerial vehicle (UAV) platforms. The most accurate method to align an orthophoto is the installation of air-photo targets at a test site prior to flight image acquisition, and these targets were used as ground control points (GCPs) for georeferencing and georectification. However, there are time and cost limitations related to installing the targets and conducting field surveys on the targets during every flight.
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Chen, Guo, Zhigui Liu, Guang Yu, and Jianhong Liang. "A New View of Multisensor Data Fusion: Research on Generalized Fusion." Mathematical Problems in Engineering 2021 (October 15, 2021): 1–21. http://dx.doi.org/10.1155/2021/5471242.

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Multisensor data generalized fusion algorithm is a kind of symbolic computing model with multiple application objects based on sensor generalized integration. It is the theoretical basis of numerical fusion. This paper aims to comprehensively review the generalized fusion algorithms of multisensor data. Firstly, the development and definition of multisensor data fusion are analyzed and the definition of multisensor data generalized fusion is given. Secondly, the classification of multisensor data fusion is discussed, and the generalized integration structure of multisensor and its data acquisi
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Dissertations / Theses on the topic "Multisensor"

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Filippidis, Arthur. "Multisensor data fusion." Title page, contents and abstract only, 1993. http://web4.library.adelaide.edu.au/theses/09ENS/09ensf482.pdf.

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Purohit, Madhavi. "Multisensor integration for a robot." Ohio : Ohio University, 1989. http://www.ohiolink.edu/etd/view.cgi?ohiou1182456473.

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Petrovic, Vladimir. "Multisensor pixel-level image fusion." Thesis, University of Manchester, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.715412.

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Pradhan, Pushkar S. "Multiresolution based, multisensor, multispectral image fusion." Diss., Mississippi State : Mississippi State University, 2005. http://library.msstate.edu/etd/show.asp?etd=etd-07082005-140541.

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Jeong, Soonho Tugnait Jitendra K. "Topics in multisensor maneuvering target tracking." Auburn, Ala., 2005. http://repo.lib.auburn.edu/2005%20Summer/doctoral/JEONG_SOONHO_43.pdf.

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Bonaccorso, Filippo. "Multisensor data fusion for robotic control." Doctoral thesis, Università di Catania, 2012. http://hdl.handle.net/10761/1081.

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Nowadays robots perform more and more tasks, from simpler ones such as automatic domestic vacuum cleaner, to highly skilled ones such as tele-surgery. To do so a robot continuously needs to know what to do and what has been done. A user controls robots through a system controller, which processes both his commands and information from the environment. These information are collected by a measurement system, often called observer, which bring back them to the system controller. Measures are the information used to perform the control. The simpler measurement system can, in general, be considere
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PINARDI, STEFANO. "Movements recognition with intelligent multisensor analysis." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/19297.

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In movement science with inertial sensor many different methodologies resolving specific aspects of movement recognition have been proposed. They are very interesting, and useful, but none of them are generally explicative of what is going on in the semantic sense. When we go down to the movement recognition/classification area (for example in Ambient Intelligence) we do not have a feasible model that can be considered generally predictive or usable for activity recognition. Also, in the field of movement recognition with inertial sensors many technological issues arise: technological div
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Rødningsby, Anders. "Multitarget Multisensor Trackingin the Presence of Wakes." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11913.

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TARGET tracking is an essential requirement for surveillance and control systems to interpret the environment. This environment may contain multiple targets, and the environmental information may be obtained by multiple sensors in a multitarget multisensor tracking system. In this thesis we focus on targets which, in addition to reflecting signals themselves, also have a trailing path behind them, called a wake. This wake causes additional measurements to those originating from the target. When the measurements are processed, the estimated track can be misled and sometimes lose the real target
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Berg, Timothy Martin. "Model distribution in decentralized multisensor data fusion." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317852.

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Hilmersson, Anette. "Multisensor Stress Monitoring For Non-Stationary Subjects." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-28340.

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Monitoring stress in real-time, in a non-laboratory environment can be benecial in several applications. One of these, which have been the motivation for this thesis, is to to perform this measurement during Attention decit hyperactivity disorder (ADHD) diagnosis. Monitoring several physiological responses to internal or external stimuli in a single soft-real-time system is nota solution widely used in an application like this. The thesis starts by studying several stress related responses in detail. Sensors for all of the responses are not implemented nor is it possible toimplement in to the
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Books on the topic "Multisensor"

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Hyder, A. K., E. Shahbazian, and E. Waltz, eds. Multisensor Fusion. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2.

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NATO Advanced Study Institute on Multisensor Data Fusion (2000 Pitlochry, Scotland). Multisensor fusion. Kluwer Academic Publishers, 2002.

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Foresti, Gian Luca, Carlo S. Regazzoni, and Pramod K. Varshney, eds. Multisensor Surveillance Systems. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0371-2.

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James, Llinas, ed. Multisensor data fusion. Artech House, 1990.

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Aggarwal, J. K., ed. Multisensor Fusion for Computer Vision. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-02957-2.

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Zhu, Yunmin. Multisensor Decision And Estimation Fusion. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1045-1.

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1936-, Aggarwal J. K., North Atlantic Treaty Organization. Scientific Affairs Division., and NATO Advanced Research Workshop on Multisensor Fusion for Computer Vision (1989 : Grenoble, France), eds. Multisensor fusion for computer vision. Springer-Verlag, 1993.

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Zhu, Yunmin. Multisensor Decision And Estimation Fusion. Springer US, 2003.

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Appriou, Alain. Uncertainty Theories and Multisensor Data Fusion. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118578636.

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Yan, Liping, Lu Jiang, and Yuanqing Xia. Multisensor Fusion Estimation Theory and Application. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9426-7.

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Book chapters on the topic "Multisensor"

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Grejner-Brzezinska, Dorota A. "Multisensor Systems." In Encyclopedia of Geodesy. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-02370-0_12-1.

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Cardone, Mauro. "Multisensor Fusion." In Automated and Autonomous Navigation Powered by GNSS. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-78753-9_1.

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Hanwehr, R. "Information Fusion in the Human Brain." In Multisensor Fusion. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_1.

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Valin, P. "Random Sets and Unification." In Multisensor Fusion. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_10.

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Bloch, I. "Fusion of Information under Imprecision and Uncertainty, Numerical Methods, and Image Information Fusion." In Multisensor Fusion. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_11.

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Rao, N. S. V. "Multisensor Fusion under Unknown Distributions Finite-Sample Performance Guarantees." In Multisensor Fusion. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_12.

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Cadre, J. P. "Data Association and Multitarget Tracking." In Multisensor Fusion. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_13.

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Ranchin, T. "Wavelets for Modeling and Data Fusion in Remote Sensing." In Multisensor Fusion. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_14.

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Nimier, V. "Soft Sensor Management for Multisensor Tracking Algorithm." In Multisensor Fusion. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_15.

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Bekmuratov, T. E. "Intellectualization of Multi-Sensor Systems for Decision Making Process." In Multisensor Fusion. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_16.

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Conference papers on the topic "Multisensor"

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Kaufmann, Alexander, Andrey Kravchenko, Sophie Emperhoff, et al. "Monolithically integrated wireless multisensor solution." In 2024 Smart Systems Integration Conference and Exhibition (SSI). IEEE, 2024. http://dx.doi.org/10.1109/ssi63222.2024.10740545.

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Pate, Michael A. "Multisensor Boresighting." In OPTCON '88 Conferences--Applications of Optical Engineering, edited by Thomas C. Bristow and Alson E. Hatheway. SPIE, 1989. http://dx.doi.org/10.1117/12.950992.

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Pao, Lucy. "Distributed multisensor fusion." In Guidance, Navigation, and Control Conference. American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-3549.

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Romeo, Katerin, Piet B. W. Schwering, and Marcel G. J. Breuers. "Multisensor track fusion." In Photonics West 2001 - Electronic Imaging, edited by Bernd Girod, Charles A. Bouman, and Eckehard G. Steinbach. SPIE, 2000. http://dx.doi.org/10.1117/12.411821.

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Sienski, Kenneth T., and Stephen T. Makrinos. "Airborne multisensor system." In Aerospace/Defense Sensing and Controls, edited by Raja Suresh and William Langford. SPIE, 1996. http://dx.doi.org/10.1117/12.242075.

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de Salabert, Arturo, Timothy K. Pike, F. G. Sawyer, et al. "Multisensor image processing." In Munich '91 (Lasers '91), edited by Hatem N. Nasr. SPIE, 1991. http://dx.doi.org/10.1117/12.46065.

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Colombi, John M., D. Krepp, Steven K. Rogers, Dennis W. Ruck, and Mark E. Oxley. "Multisensor user authentication." In Optical Engineering and Photonics in Aerospace Sensing, edited by Steven K. Rogers. SPIE, 1993. http://dx.doi.org/10.1117/12.152518.

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Colombi, John M., D. Krepp, Steven K. Rogers, Dennis W. Ruck, and Mark E. Oxley. "Multisensor user authentication." In Optical Engineering and Photonics in Aerospace Sensing, edited by Dennis W. Ruck. SPIE, 1993. http://dx.doi.org/10.1117/12.152612.

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Mitchell, Horace. "Multisensor fire observations." In ACM SIGGRAPH 2003 video review. ACM Press, 2003. http://dx.doi.org/10.1145/1006114.1006117.

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Henderson, Thomas C. "Multisensor Knowledge Systems." In 1988 Technical Symposium on Optics, Electro-Optics, and Sensors, edited by Charles B. Weaver. SPIE, 1988. http://dx.doi.org/10.1117/12.946669.

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Reports on the topic "Multisensor"

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Foyle, David C. Multisensor Evaluation Framework. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada224271.

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Blair, William Dale. Information-Based Multisensor Detecton. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada397420.

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Blair, William Dale. Information-Based Multisensor Detection. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada369381.

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Bar-Shalom, Yaakov, K. R. Pattipati, and P. K. Willett. Estimation With Multisensor Fusion. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada416565.

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Bar-Shalom, Y., and K. R. Pattipati. Multisensor/Multiscan Detection Fusion. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada336763.

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Blair, William D. Information-Based Multisensor Detection. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada383793.

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Aggarwal, R. K., M. Bazakos, J. Budenske, Y. Kim, and S. Mader. Hierarchical Multisensor Image Understanding. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada160324.

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Minor, Christian P., Joseph C. Gezo, and Kevin J. Johnson. Information Measures for Multisensor Systems. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada591226.

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Yocky, D. A., M. D. Chadwick, S. P. Goudy, and D. K. Johnson. Multisensor data fusion algorithm development. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/172138.

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Hall, David L., and Alan Steinberg. Dirty Secrets in Multisensor Data Fusion. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada394631.

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